Foundations and Evaluations in NLP
This work addresses challenges in NLP evaluation and resource development, particularly for morphologically rich languages like Korean, with broader implications for multilingual systems, though it is incremental in building on existing methods.
This memoir tackles the problem of evaluating NLP systems by proposing the jp-algorithm, a novel alignment-based framework that overcomes limitations of traditional methods, achieving enhanced accuracy and flexibility for tasks like tokenization and sentence boundary detection. It also develops a morpheme-based annotation scheme for Korean, achieving state-of-the-art results in tasks such as part-of-speech tagging and dependency parsing.
This memoir explores two fundamental aspects of Natural Language Processing (NLP): the creation of linguistic resources and the evaluation of NLP system performance. Over the past decade, my work has focused on developing a morpheme-based annotation scheme for the Korean language that captures linguistic properties from morphology to semantics. This approach has achieved state-of-the-art results in various NLP tasks, including part-of-speech tagging, dependency parsing, and named entity recognition. Additionally, this work provides a comprehensive analysis of segmentation granularity and its critical impact on NLP system performance. In parallel with linguistic resource development, I have proposed a novel evaluation framework, the jp-algorithm, which introduces an alignment-based method to address challenges in preprocessing tasks like tokenization and sentence boundary detection (SBD). Traditional evaluation methods assume identical tokenization and sentence lengths between gold standards and system outputs, limiting their applicability to real-world data. The jp-algorithm overcomes these limitations, enabling robust end-to-end evaluations across a variety of NLP tasks. It enhances accuracy and flexibility by incorporating linear-time alignment while preserving the complexity of traditional evaluation metrics. This memoir provides key insights into the processing of morphologically rich languages, such as Korean, while offering a generalizable framework for evaluating diverse end-to-end NLP systems. My contributions lay the foundation for future developments, with broader implications for multilingual resource development and system evaluation.